SKLGP大讲堂第115期 | Alexander Strom & Filippo Catani

报告题目1：Rock avalanches: Basic characteristics and discrimination from other mass wasting phenomena

报告人：Alexander Strom

单位：地球动力学研究中心，俄罗斯

时间：2025年5月22日14:30—15:30（周四）

地点：全重实验室211会议室

报告人简介：

Dr. Alexander Strom graduated from the Geological department of Moscow State University in 1975. He is the chief expert of the Research institute of Energy Structures – branch of JSC "Hydro project Institute", Moscow, Russia; professor of the Engineering Geology Department of the Sergo Ordzhonikidze Russian State University for Geological Prospecting, and visiting professor in SKLGP, Chengdu, China. Studied large rockslides and performed field paleo seismological and seismotectonic investigations in the Tien Shan, Pamirs, Garhwal Himalayas, Great Caucasus, Western and Eastern Sayan mountains, Far East of Russia, Sakhalin, Mongolia and Northern Sudan. Translated in Russian the second edition of "Paleo seismology" and organized its publication in Russia. Collected the world-wide data base on seismic surface ruptures and studied hazards associated with active fault that cross trunk pipelines in Sakhalin and in Eastern Siberia. Performed detail studies of morphological and structural features typical of rock slides and rock avalanches in the Central Asia region, Caucasus and other mountainous regions. Since 2006 have organized annual field training course on Rockslides and related phenomena in Kyrgyzstan. Compiled the database of large-scale rockslides in the Central Asia region and wrote a book “Rockslides and rock avalanches of Central Asia” published by Elsevier in 2018.

报告内容简介：

The definition of the term “rock avalanche” will be presented along with the general characteristic features of their deposits, such as: a) the extremely high velocity of their motion; b) intensive fragmentation and inverse grain-size composition with coarse carapace and fragmented interiors; c) preservation of the host rock mass structure (no mixing of debris). The importance of these peculiarities for understanding of rock avalanche motion mechanism will be discussed. as well as rock avalanche deposits discrimination from the deposits left by other mass-wasting phenomena, such as moraines, rock glaciers and debris flows.。

报告题目2：Landslide Prediction: What We Know and What We Would Like to Know (With the Help of AI)

报告人：Filippo Catani

单位：帕多瓦大学，意大利

时间：2025年5月22日15:30—16:30（周四）

地点：全重实验室211会议室

报告人简介：

Filippo Catani, PhD, is Full Professor of Engineering Geology with the Department of Geosciences of the University of Padova, Director of the “Machine Intelligence and Slope Stability Laboratory” (MISSlab), and President of the BSc Program in “Earth and Climate Dynamics” at the University of Padova. Since 2016, he is UNESCO Chair Associate on the Prevention and Sustainable Management of Geo-Hydrological Hazards at the University of Florence (Italy). He is Invited Professor of Landslide Monitoring and Early Warning at the Korean Institute of Geoscience and Mineral Resources (KIGAM, South Korea), and at the SKLGP of Chengdu University of Technology (China). He is Council Member of FOMLIG (Future of Machine Learning in Geotechnics, est. 2023) and member of the NASA-JPL informal group on Earth Surface Changes. He is member of the Scientific Committee of the Centre of Civil Protection of the University of Florence, ERASMUS Program Delegate for the Master Program in Water and Geological Risk Engineering at the University of Padova, and member of the Scientific Board of the Italian Branch of IAEG. He is Editor for the NHESS journal. His recent research interests are focused on the application of artificial intelligence methods to slope stability, the combination of numerical modelling and deep learning for the analysis of failure conditions on natural and man-made slopes, and the use of advanced remote sensing methods for slope monitoring and landslide forecasting at all scales.

报告内容简介：

Landslide prediction represents, as one might easily imagine, one of the key capabilities we must possess for the reduction of hydrogeological risk, as it is a fundamental prerequisite. Despite the importance of the topic, our ability to make predictions that are operationally useful remains partial and faces many challenges, which the scientific community is striving to reduce or eliminate.

What does landslide prediction involve? The answer lies in the need to foresee or estimate all factors that make a landslide a risk to the territory and its inhabitants. It is therefore not enough to determine whether a particular slope is more unstable than others (spatial prediction).We must also be able to predict the timing (or probability within a given time frame) when this potential instability may turn into an actual triggering (temporal prediction). Furthermore, if we want to estimate the potential damage that such a landslide, once triggered, could cause, we need to estimate its destructive energy (intensity prediction). Lastly, to understand which elements at risk could be affected by the landslide’s evolution, we also need to predict its propagation from the triggering point (runout prediction).

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